DETAILED ACTION
This action is made FINAL in response to the amendments filed on 12/09/2025.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 – 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step One
The claims are directed to a method (claims 1 - 14) and a non-transitory computer readable medium (claims 15 - 20 ). Thus, each of the claims falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
As to claims 1 and 15,
Step 2A, Prong One
The claim recites in part:
inferring, by the trained process machine learning model running on a hardware computer system, a predicted integrated circuit
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. “Inferring” is the mental process of drawing conclusions based on data that cannot be directly observed. For example, a human can “infer a predicted integrated circuit” simply by using existing knowledge predicted integrated circuit patterns or past experiences of the integrated circuit pattern processes.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
obtaining (i) trained a process machine learning model configured to predict am integrated circuit pattern on a substrate as produced using a semiconductor pattering process, and (ii) a target pattern
which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
The claim further recites:
training, by the hardware computer system, a mask pattern machine learning model configured to predict a mask pattern that is configured to interact with radiation to create an image from the radiation to expose a resist layer the substrate using the semiconductor patterning process, the training performed based on the predicted integrated circuit pattern and a cost function that determines a difference between the predicted integrated circuit pattern and the target pattern,
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
The claim further recites:
wherein the mask pattern machine learning model provides optical proximity correction and/or source mask optimization of the mask pattern.
these elements are recited at a high-level of generality and amounts to no more than adding the words “apply it” to the judicial exception. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)).
The hardware computer system, substrate, integrated circuit, resist layer, and semiconductor are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
The recitation of trained process machine learning model, integrated circuit pattern, target pattern, cost function, optical proximity, mask pattern machine learning model, and source mask optimization amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
obtaining (i) trained a process machine learning model configured to predict am integrated circuit pattern on a substrate as produced using a semiconductor pattering process, and (ii) a target pattern
are recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
The claim further recites:
training, by the hardware computer system, a mask pattern machine learning model configured to predict a mask pattern that is configured to interact with radiation to create an image from the radiation to expose a resist layer the substrate using the semiconductor patterning process, the training performed based on the predicted integrated circuit pattern and a cost function that determines a difference between the predicted integrated circuit pattern and the target pattern,
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
The claim further recites:
wherein the mask pattern machine learning model provides optical proximity correction and/or source mask optimization of the mask pattern.
are recited at a high-level of generality and amounts to no more than adding the words “apply it” to the judicial exception. These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”).
The hardware computer system, substrate, integrated circuit, resist layer, and semiconductor are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
The recitation of trained process machine learning model, integrated circuit pattern, target pattern, cost function, optical proximity, mask pattern machine learning model, and source mask optimization amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claims 2 and 16,
Step 2A, Prong One
The claim does not recite an abstract idea or any other judicial exception and therefore passes Step 2A, Prong of the Alice/Mayo analysis.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the training mask pattern machine learning model comprises iteratively modifying one or more parameters of the mask pattern machine learning model based on a gradient-based method such that the cost function is reduced
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
The recitation gradient-based method amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. The limitations:
wherein the training mask pattern machine learning model comprises iteratively modifying one or more parameters of the mask pattern machine learning model based on a gradient-based method such that the cost function is reduced
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
The recitation gradient-based method amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception
As to claims 2 and 16, The recitation of “wherein the training mask pattern machine learning model comprises iteratively modifying one or more parameters of the mask pattern machine learning model based on a gradient-based method such that the cost function is reduced” amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
As to claims 3 and 17, The recitation of “wherein the gradient based method generates a gradient map indicating whether the one or more parameters be modified such that the cost function is reduced” amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
As to claim 4, The recitation of “wherein the cost function is minimized” amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
As to claim 5, The recitation of “wherein the cost function represents an edge placement error between the target pattern and the predicted integrated circuit pattern” amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
As to claim 6, The recitation of “wherein the cost function represents a mean square error between the target pattern and the predicted integrated circuit pattern
and/or difference in a critical dimension” amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
As to claims 7 and 19,
Step 2A, Prong One
The claim does not recite an abstract idea or any other judicial exception and therefore passes Step 2A, Prong of the Alice/Mayo analysis.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
the process machine learning model comprises:
(i) a first trained machine learning model configured to predict a mask transmission of the patterning process; and/or
(ii) a second trained machine learning model configured to be coupled to the first trained model and configured to predict an optical behavior of an apparatus used in the patterning process; and/or
(iii) a third trained machine learning model configured to be coupled to the second trained model and configured to predict a resist process of the patterning process
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. The limitations:
the process machine learning model comprises:
(i) a first trained machine learning model configured to predict a mask transmission of the patterning process; and/or
(ii) a second trained machine learning model configured to be coupled to the first trained model and configured to predict an optical behavior of an apparatus used in the patterning process; and/or
(iii) a third trained machine learning model configured to be coupled to the second trained model and configured to predict a resist process of the patterning process
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception
As to claim 8,
Step 2A, Prong One
The claim does not recite an abstract idea or any other judicial exception and therefore passes Step 2A, Prong of the Alice/Mayo analysis.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
the trained process machine learning model comprises the first trained machine learning model and the first trained machine learning model comprises a machine learning model configured to predict a two dimensional mask transmission effect or a three dimensional mask transmission effect of the patterning process
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. The limitations:
the trained process machine learning model comprises the first trained machine learning model and the first trained machine learning model comprises a machine learning model configured to predict a two dimensional mask transmission effect or a three dimensional mask transmission effect of the patterning process
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception
As to claim 9,
Step 2A, Prong One
The claim does not recite an abstract idea or any other judicial exception and therefore passes Step 2A, Prong of the Alice/Mayo analysis.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the trained process machine learning model comprises the first, second and third machine learning models,
wherein the first trained machine learning model receives a mask image corresponding to the target pattern and predicts a mask transmission image,
wherein the second trained machine learning model receives the predicted mask transmission image and predicts an aerial image, and
wherein the third trained machine learning model receives the predicted aerial image and predicts a resist image, wherein the resist image includes the predicted integrated circuit pattern on the substrate.
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. The limitations:
wherein the trained process machine learning model comprises the first, second and third machine learning models,
wherein the first trained machine learning model receives a mask image corresponding to the target pattern and predicts a mask transmission image,
wherein the second trained machine learning model receives the predicted mask transmission image and predicts an aerial image, and
wherein the third trained machine learning model receives the predicted aerial image and predicts a resist image, wherein the resist image includes the predicted integrated circuit pattern on the substrate.
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception
As to claims 10 and 20, The recitation of “wherein the mask pattern machine learning model configured to predict the mask pattern is a convolutional neural network” amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
As to claim 11, The recitation of “wherein the mask pattern comprises optical proximity corrections including assist features” amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
As to claim 12, The recitation of “wherein the optical proximity corrections are in the form of a mask image and the training is based on the mask image or pixel data of the mask image, and an image of the target pattern” amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
As to claim 13, The recitation of “wherein the mask image is a continuous transmission mask image” amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
As to claim 14,
Step 2A, Prong One
The claim does not recite an abstract idea or any other judicial exception and therefore passes Step 2A, Prong of the Alice/Mayo analysis.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
optimizing a predicted mask pattern, predicted by the trained mask pattern machine learning model, by iteratively modifying one or more mask variables of the predicted mask pattern, an iteration comprising:
predicting, via simulation of a physics based or a machine learning based mask model, a mask transmission image based on the predicted mask pattern;
predicting, via simulation of a physics based or a machine learning based optical model, an optical image based on the mask transmission image;
predicting, via simulation of a physics based or a machine learning based resist model, a resist image based on the optical image;
evaluating the cost function based on the resist image; and
modifying, via simulation, one or more mask variables associated with the predicted mask pattern based on a gradient of the cost function such that the cost function is reduced.
these elements are recited at a high-level of generality and amounts to no more than adding the words “apply it” to the judicial exception. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)).
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. The limitations:
optimizing a predicted mask pattern, predicted by the trained mask pattern machine learning model, by iteratively modifying one or more mask variables of the predicted mask pattern, an iteration comprising:
predicting, via simulation of a physics based or a machine learning based mask model, a mask transmission image based on the predicted mask pattern;
predicting, via simulation of a physics based or a machine learning based optical model, an optical image based on the mask transmission image;
predicting, via simulation of a physics based or a machine learning based resist model, a resist image based on the optical image;
evaluating the cost function based on the resist image; and
modifying, via simulation, one or more mask variables associated with the predicted mask pattern based on a gradient of the cost function such that the cost function is reduced.
are recited at a high-level of generality and amounts to no more than adding the words “apply it” to the judicial exception. These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception
As to claim 18, The recitation of “wherein the cost function represents an edge placement error between the target pattern and the predicted integrated circuit pattern, a mean square error between the target pattern and the predicted integrated circuit pattern and/or a difference in a critical dimension.” amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
Response to Arguments
Applicant's arguments filed 12/09/2025 have been fully considered but they are not persuasive.
Claim Rejections - 35 USC § 102 & 103
The newly added limitations overcome the 103 Rejections and the 103 Rejections have been withdrawn
Claim Rejections - 35 USC § 101
The amendment claims still do not overcome the 101 rejection as the claims are abstract and do not show an improvement of technology and/or an improvement of a computer program. The steps described in the claims can be completed by a mental process and/or a generic computer component.
The Applicant argues:
Applicant submits that the claimed subject matter is essentially identical in terms of patent subject matter eligibility as Example 39 - Method for Training a Neural Network for Facial Detection¹ provided by the USPTO for understanding of the USPTO's "2019 Revised Patent Subject Matter Eligibility Guidance", 84 FR 50 (hereinafter the "2019 PEG") and the USPTO's guidance in MPEP, $$2103-2106.07(c).
Applicant submits that there is no difference in terms of patent subject matter eligibility between the claimed training of the machine learning model in the claimed subject matter and the training of a machine learning model of Example 39.
In short, the claim of Example 39 claims obtaining a trained machine learning model trained in a first stage (i.e., "training the neural network in a first stage using the first training set" following some prefatory data collection). With output from the trained machine learning model trained in the first stage, there is training in a second stage using a training set that comprises output from that trained machine learning model (i.e.,"creating a second training set for a second stage of training comprising the first training set and digital non-facial images that are incorrectly detected as facial images after the first stage of training" and "training the neural network in a second stage using the second training set.").
Applicant submits that the claimed subject matter is of essentially the same form as the claim of Example 39, except that the claimed subject matter (i.e., prediction of a mask pattern to make an integrated circuit) relates to a different technical field than Example 39 (i.e., facial detection) and employs a different technique. In particular:
Like Example 39, claim recites obtaining a trained machine learning
trained in a first stage. In claim 1, that is "a trained process machine learning model configured to predict an integrated circuit pattern on a substrate as produced using a semiconductor patterning process."
Like Example 39, the claim recites obtaining training data a trained
machine learning model of first stage and training a machine learning model in a second stage using that data. In claim 1, that is "inferring, by the trained process machine learning model, a predicted integrated circuit pattern and training a mask pattern machine learning model the training performed based on the predicted integrated circuit pattern " (emphasis added)
While, in Example 39, the training in the second stage involves an obvious use of "images that are incorrectly detected as facial images after the first stage of training," the claimed subject matter here adopts an entirely different and new approach of training a machine learning model with data different than it predicts. That is, the mask pattern machine learning model is configured to predict a mask pattern that is configured to interact with radiation to create an image from the radiation to expose a resist layer on the substrate using the semiconductor patterning process. In stark contrast, the trained process machine learning model configured to predict an integrated circuit pattern on a substrate as produced using a semiconductor patterning process. As claimed, the mask machine learning model is trained based on the predicted pattern from the process machine learning model while it predicts an entirely different pattern - a mask pattern that is configured to interact with radiation to create an image from the radiation to expose a resist layer on the substrate using the semiconductor patterning process. This technique can provide, for example, "(i) a better fitting compared to the physics based or empirical model due to higher fitting power (i.e., relatively more number parameters such as weights and bias may be adjusted) of the machine leaning model, and (ii) simpler gradient computation compared to the traditional physics based or empirical models (i) improved accuracy of prediction of, for example, a mask pattern or a substrate pattern, (ii) substantially reduced runtime (e.g., by more than 10x, 100x, etc.) for any design layout for which a mask layout may be determined, and (iii) simple gradient computation compared to physics based model, which may also improve the computation time of the computer(s) used in the patterning process." Specification, paragraph [0092].
The examiner strongly disagrees. Firstly, the applicant states “Applicant submits that the claimed subject matter is of essentially the same form as the claim of Example 39, except that the claimed subject matter (i.e., prediction of a mask pattern to make an integrated circuit) relates to a different technical field than Example 39 (i.e., facial detection) and employs a different technique” then follows that up with “the claimed subject matter here adopts an entirely different and new approach of training a machine learning model with data different than it predicts” which is very confusing and doesn’t make any sense. The Applicant does not accurately explain how the cited example is relevant to the “new approach of training a machine learning model with data different than it predicts” of presently claimed invention. The example is not tied to the claimed features, nor is any accurate comparison provided demonstrating how it supports patent eligibility. It is unclear why the Applicant relies on this example.
Secondly, examiner respectfully disagrees with the applicant’s position, as the arguments presented rely on limitations that are neither explicitly recited in the claims nor reasonably inferred from them. At no point in the pending claims does the appellant assert, describe, or even suggest the limitations of “This technique can provide, for example, "(i) a better fitting compared to the physics based or empirical model due to higher fitting power (i.e., relatively more number parameters such as weights and bias may be adjusted) of the machine leaning model, and (ii) simpler gradient computation compared to the traditional physics based or empirical models (i) improved accuracy of prediction of, for example, a mask pattern or a substrate pattern, (ii) substantially reduced runtime (e.g., by more than 10x, 100x, etc.) for any design layout for which a mask layout may be determined, and (iii) simple gradient computation compared to physics based model, which may also improve the computation time of the computer(s) used in the patterning process.” Rather, the applicant appears to have introduced this language as part of the argument, but such a limitation cannot be read into the claims when it is not supported by the actual claim language.
Thirdly, the applicant argues that a mask pattern prediction learning model is “trained,” yet fails to adequately explain how the limitation “predict a mask pattern that is configured to interact with radiation to create an image from the radiation to expose a resist layer the substrate using the semiconductor patterning process” constitutes any technological improvement. The claimed limitations do not clarify how the model’s predictions impact or improve the lithographic process. For example, it is unclear what occurs when a predicted mask pattern closely matches a target versus when it deviates significant
Further, the Applicant does not define the cost function, nor explain how differences in the cost function are interpreted (e.g. whether a larger or smaller difference reflects improved performance). There is also no description of how such differences are used to update or retrain the model. The claimed limitations fails to describe any element which incorporates feedback, such as a resist layer exposure results or cost function outputs, to eliminate false predictions or refine mask patterns.
Lastly, the applicant is silent as to how the mask pattern machine learning model provides optical proximity correction or optimizes mask patterns in a way that improves technology. Rather the claims just perform an abstract analysis and merely describe an intended result without providing the steps or elements necessary to achieve such results.
The Applicant argues:
The claimed subject matter is as eligible as the subject matter found eligible by the Examiner in Ex Parte Desjardins
Even if the claimed subject matter is directed to an abstract idea (which Applicant does not concede), Applicant submits that the claimed subject matter is directed to a "practical application." In particular, the claimed technique identifies an artificial intelligence technique that can provide, for example, "(i) a better fitting compared to the physics based or empirical model due to higher fitting power (i.e., relatively more number parameters such as weights and bias may be adjusted) of the machine leaning model, and (ii) simpler gradient computation compared to the traditional physics based or empirical models. (i) improved accuracy of prediction of, for example, a mask pattern or a substrate pattern, (ii) substantially reduced runtime (e.g., by more than 10x, 100x, etc.) for any design layout for which a mask layout may be determined, and (iii) simpler gradient computation compared to physics based model, which may also improve the computation time of the computer(s) used in the patterning process.” Specification, paragraph [0092]. Providing an artificial intelligence technique that provides one or more of these technical and practical benefits is surely a "practical application."
The analysis here is essentially the same as admonished by the new Director in the precedential decision issued in U.S. patent application no. 16/319,040. The Director there said that:
under the panel's reasoning, many AI innovations are potentially unpatentable-even if they are adequately described and nonobvious- because the panel essentially equated any machine learning with an unpatentable "algorithm" and the remaining additional elements as "generic computer components," without adequate explanation. Dec. 24. Examiners and panels should not evaluate claims at such a high level of generality.
In this case, the claims are rejected with the same type of considerations - "recited at a high-level of generality and amounts to no more than adding the words 'apply it' to the judicial exception", "recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component" and generally linking the use of the judicial exception to a particular environment of field of use." When the claim language is viewed as whole (rather than dissected into parts), it is apparent that the claimed subject matter is directed to a "practical application" of an artificial intelligence technique to provide, for example, "(i) a better fitting compared to the physics based or empirical model due to higher fitting power (i.e., relatively more number parameters such as weights and bias may be adjusted) of the machine leaning model, and (ii) simpler gradient computation compared to the traditional physics based or empirical models (i) improved accuracy of prediction of, for example, a mask pattern or a substrate pattern, (ii) substantially reduced runtime (e.g., by more than 10x, 100x, etc.) for any design layout for which a mask layout may be determined, and (iii) simpler gradient computation compared to physics based model, which may also improve the computation time of the computer(s) used in the patterning process." Specification, paragraph [0092].
The examiner strongly disagrees. The applicant argues that the claims are directed to a “practical application” based on alleged improvements such as better fitting, improved prediction accuracy, reduced runtime, and simpler gradient computation. However, these are intended results, not claim limitations. The claims do not recite how these improvements are achieved but instead broadly rely on generic machine operations like adjusting weights/biases and performing gradient calculations. There is no indication that the claims improve the functioning of the computer itself or any other technology, an improvement to an abstract idea itself does not amount to a practical application. Claiming the ‘improved speed or accuracy” is inherent with applying the abstract idea on a computer and does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).
The argument regarding to Ex parte Desjardins and the Director’s statements is also not persuasive. The claims are evaluated based on their own language and the claims are written at a high level of generality and amount to no more than applying an algorithm using generic computing components.
It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)) in subsection II, below. In addition, the improvement can be provided by the additional element(s) in combination with the recited judicial exception. See MPEP § 2106.04(d) (discussing Finjan, Inc. v. Blue Coat Sys., Inc., 879 F.3d 1299, 1303-04, 125 USPQ2d 1282, 1285-87 (Fed. Cir. 2018))
It is important to keep in mind that an improvement in the abstract idea itself is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology (MPEP 2106.05(a)(II).
The Applicant argues:
The claimed subject matter recites limitations that cannot be practically
performed in the human mind Applicant submits that the claimed subject matter recites limitations that cannot be practically performed in the mind and thus the claimed subject matter is directed to patent-eligible subject matter. MPEP, §2106. For example, given a semiconductor patterning process, it is not reasonable that someone in their mind can predict in what manner an integrated circuit pattern on a substrate would be produced using such a semiconductor patterning process. A semiconductor patterning process involves, for example, thousands, if not millions, of complex optical, chemical and mechanical actions that cannot reasonably be processed in a human (or even with the aid of a pencil and paper) to determine how a mask pattern realized an integrated circuit pattern. Similarly, it is not reasonable that someone in their mind can predict a mask pattern that is configured to interact with radiation to create an image from the radiation to expose a resist layer on the substrate using a semiconductor patterning process, wherein that prediction also provides optical proximity correction and/or source mask optimization of the mask pattern. A semiconductor patterning process involves, for example, thousands, if not millions, of complex optical, chemical and mechanical actions that cannot reasonably be processed in a human (or even with the aid of a pencil and paper) to determine how to achieve a mask pattern that realizes a desired integrated circuit pattern on a substrate and the thousands, if not millions, of possible changes that can be made to a mask pattern and/or illumination for that mask pattern to provide optical proximity correction and/or source mask optimization of the mask pattern. Furthermore, the inferring and execution of a machine learning model is impossible to be performed in the mind since it is, by definition, a machine. So, Applicant submits that the claimed subject matter recites limitations that cannot be practically performed in the mind and thus the claimed subject matter is directed to
patent-eligible subject matter.
The examiner strongly disagrees. The arguments presented rely on limitations that are neither explicitly recited in the claims nor reasonably inferred from them. At no point in the pending claims does the appellant assert, describe, or even suggest the limitations of “A semiconductor patterning process involves, for example, thousands, if not millions, of complex optical, chemical and mechanical actions that cannot reasonably be processed in a human (or even with the aid of a pencil and paper) to determine how a mask pattern realized an integrated circuit pattern” Rather, the applicant appears to have introduced this language as part of the argument, but such a limitation cannot be read into the claims when it is not supported by the actual claim language.
The claims are not directed to the physical semiconductor fabrication process itself, but rather to the abstract idea of predicting or determining a mask pattern using a machine learning model. The Applicant’s arguments improperly focuses the potential complexity and scale of the physical process rather than the actual claimed limitations. The potential of the claimed computations may involve large datasets or complex calculations does not remove them from being mental processes. Limiting applications of the abstract idea to only “small” datasets on “non-complex” calculations is simply an attempt to limit the use of the abstract idea to a particular technological environment, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 22016).
The recitation of a “machine learning model,” by itself, does not make the claims patent-eligible as the claims do not provide specific details regarding how the model is trained, updated, or improved in a way that results in a technological improvement. The claims does not include additional elements that are sufficient to amount to significantly more that the judicial exception. The additional elements of “a trained machine learning model or a mask pattern machine learning model” amount to no more than mere instructions to apply the exception using generic components which does not provide an inventive concept (See MPEP 2106.05(f)).
Lastly, the applicant is silent as to how the mask pattern machine learning model provides optical proximity correction or optimizes mask patterns in a way that improves technology. Rather the claims just perform an abstract analysis and merely describe an intended result without providing the steps or elements necessary to achieve such results. The claims lack additional elements that integrate the abstract idea into a practical application or amount to significantly more than the abstract idea itself.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/BRANDON S COLE/ Primary Examiner, Art Unit 2128